A Neural Network Technique for Re-Meshing of Bone Micro-Structure

Today there is major interest within the biomedical community in developing accurate non-invasive means for evaluation of bone micro-structure and bone quality. Recent improvements in 3D imaging technology, among them development of micro-CT and micro-MRI scanners, allow in-vivo 3D high-resolution scanning and reconstruction of large specimens or even whole bone models. Thus, the tendency today is to evaluate bone features using 3D assessment techniques rather than traditional 2D methods. For this purpose high quality meshing methods are required. However, the 3D meshes produced from current commercial systems usually are of low quality with respect to analysis and rapid prototyping. 3D model reconstruction of bone is difficult due to the complexity of bone micro-structure. The small bone features lead to a great deal of neighborhood ambiguity near each vertex. The relatively new Neural Network method for mesh reconstruction has the potential to create or remesh 3D models accurately and quickly. A Neural Network (NN), which resembles an artificial intelligence (AI) algorithm, is a set of interconnected neurons, where each neuron is capable of making an autonomous arithmetic calculation. Moreover, each neuron is affected by its surrounding neurons through the structure of the network. This paper proposes an extension of the Growing Neural Gas Neural Network (GNG NN) technique for remeshing a triangular manifold mesh that represents bone micro structure. This method has the advantage of reconstructing the surface of an n-genus freeform object without a priori knowledge regarding the original object, its topology, or its shape.